code: def get_sym_train(sym):
      data = mx.symbol.Variable(name="data")
      global F1, F2
      F1 = config.HEAD_FILTER_NUM
      F2 = F1
  
      # shared convolutional layers
      conv_fpn_feat = get_sym_conv(data, sym)
      ret_group = []
      gt_boxes = None
      if config.CASCADE > 0:
          gt_boxes = mx.sym.Variable("gt_boxes")
  
      for stride in config.RPN_FEAT_STRIDE:
          ret = get_out(
              conv_fpn_feat,
              "face",
              stride,
              config.FACE_LANDMARK,
              lr_mult=1.0,
              gt_boxes=gt_boxes,
          )
          ret_group += ret
  
      return mx.sym.Group(ret_group)
  
file_path: insightface/detection/retinaface/rcnn/symbol/symbol_common.py
imports: import mxnet
  import mxnet.ndarray
  import numpy
  from rcnn.config import config
  from rcnn.PY_OP import rpn_fpn_ohem3, cascade_refine
label_desc: Use image node_id 13 for calling a global function with example usage: get_sym_train(sym) and returns: mx